Regarding the United States, let me start with the positive. The nation’s cyclical recovery is entering its ninth year this month and appears to have room to run. Although the Great Recession was exceptionally deep and the recovery was slower than we would have liked, real GDP is now up about 12.5 percent from its pre-crisis peak, and real disposable income is up more than 13 percent (…)

And yet, despite the sustained cyclical upswing and the country’s fundamental strengths, Americans seem exceptionally dissatisfied with the economy, and indeed have been for some time. For example, those who tell pollsters that the country is “on the wrong track” consistently outnumber those who believe that America is moving “in the right direction” by about two to one. And, of course, last November Americans elected president a candidate with a dystopian view of the economy, who claimed that the “true” U.S. unemployment rate was 42 percent (…)

So why, despite the undoubted positives, are Americans so dissatisfied? The reasons are complex and not entirely economic. Without trying to be comprehensive, I’ll highlight here four worrying trends that help to explain the sour mood.

First, stagnant earnings for the median worker. Since 1979, real output per capita in the United States has expanded by a cumulative 80 percent, and yet during that time, median weekly earnings of full-time workers have grown by only about 7 percent in real terms. Moreover, what gains have occurred are attributable to higher wages and working hours for women. For male workers, real median weekly earnings have actually declined since 1979. In short, despite economic growth, the middle class is struggling to maintain its standard of living.

Second, declining economic and social mobility. One of the pillars of America’s selfimage is the idea of the American Dream, that anyone can rise to the top based on determination and hard work. However, upward economic mobility in the United States appears to have declined notably over the postwar period. For example, in a paper aptly entitled “The Fading American Dream,” Raj Chetty and coauthors studied one metric of upward mobility, the probability that a child would grow up to earn more than his or her parents. Using Census data, they found that 90 percent of Americans born in the 1940s would go on to earn more as adults than their parents did, but that only about 50 percent of those born in the 1980s would do so. Other research finds that the United States now has one of the lowest rates of intergenerational mobility among advanced economies, measured for example by the correlation between the earnings of parents and their children. For a supposedly classless society, the U.S. is doing a good job of rigidifying its class structure through means that include residential and educational segregation, social networking, and assortative mating.

The third adverse trend is the increasing social dysfunction associated with economically distressed areas and demographic groups. For example, other former Princeton colleagues of mine, Anne Case and Angus Deaton, have done important work on morbidity and mortality among white working-class Americans (more precisely, people with only a high school degree). They find that midlife mortality rates among white working-class Americans have sharply worsened, relative to other U.S. demographic groups and working-class Europeans. Case and Deaton refer to the excess mortality among the white working class as “deaths of despair,” because of the associated declines in indicators of economic and social well-being and the important role played by factors like opioid addiction, alcoholism, and suicide. Indeed, in 2015, more Americans died of drug overdoses — about 60 percent of which involved opioids — than died from auto accidents and firearms-related accidents and crimes combined (…)

The fourth and final factor I’ll highlight, closely tied to the others, is political alienation and distrust of institutions, both public and private. In particular, Americans generally have little confidence in the ability of government, especially the federal government, to fairly represent their interests, let alone solve their problems. In a recent poll, only 20 percent of Americans said they trusted the government in Washington to do what is right “just about always” or “most of the time” (…).

I’m hardly the first to observe that Trump’s election sends an important message, which I’ve summarized this evening as: sometimes, growth is not enough. Healthy aggregate figures can disguise unhealthy underlying trends. Indeed, the dynamism of growing economies can involve the destruction of human and social capital as well as the creation of new markets, products, and processes. Unaided, well-functioning markets can of course play a crucial role in facilitating economic adjustment and redeploying resources, but in a world of imperfect capital markets and public goods problems there is no guarantee that investment in skills acquisition, immigration, or regional redevelopment will be optimal or equitable. Tax and transfer policies can help support those who are displaced, but the limits on such policies include not only traditional concerns like the disincentive effects of income-based transfers but also conflicts with social norms. Notably, people can accept temporary help but transfers that look like “handouts” are often viewed with extreme suspicion or resentment. Some active interventions thus seem a necessary part of a responsive policy mix.

Providing effective help to people and communities that have been displaced by economic change is essential, but, on the other hand, we should not understate how difficult it will be. Addressing problems like the declining prime-age participation rate or the opioid epidemic will require the careful and persistent application of evidence-based policies which populist politicians, with their impatience and distrust of experts, may have little ability to carry through. Moreover, to be both effective and politically legitimate, such policies need to involve considerable local input and cooperation across different levels of government as well as cooperation of the public and private sectors. The credibility of economists has been damaged by our insufficient attention, over the years, to the problems of economic adjustment and by our proclivity toward top-down, rather than bottom-up, policies. Nevertheless, as a profession we have expertise that can help make the policy response more effective, and I think we have a responsibility to contribute wherever we can.

A small literature has argued that structural reforms can be counterproductive when interest rates are at the zero lower bound, because of disinflationary effects. I tend to agree that those ZLB effects are probably quantitatively modest. However, whether rates are at zero or not, it seems quite likely that policies that have the effect of releasing redundant labor resources could have adverse short-run effects if insufficient aggregate demand exists to re-employ those resources in a reasonable time. It’s consequently important for the content and sequencing of reforms to take into account the macroeconomic situation, as has been pointed out by the International Monetary Fund and others. Likewise, reforms can complement, but should not be viewed as a substitute for, appropriate macroeconomic policies. In particular, labor market reforms should not by themselves be expected to solve national competitiveness problems, at least not in the short term. Also needed are appropriate macroeconomic policies, especially fiscal policies, to help ensure adequate demand and remedy the underlying source of trade imbalances.

Without a structural model, empirical results are only locally valid. And you don’t really know how local “local” is. If you find that raising the minimum wage from $10 to $12 doesn’t reduce employment much in Seattle, what does that really tell you about what would happen if you raised it from $10 to $15 in Baltimore?

That’s a good reason to want a good structural model. With a good structural model, you can predict the effects of policies far away from the current state of the world.

In lots of sciences, it seems like that’s exactly how structural models get used. If you want to predict how the climate will respond to an increase in CO2, you use a structural, microfounded climate model based on physics, not a simple linear model based on some quasi-experiment like a volcanic eruption. If you want to predict how fish populations will respond to an increase in pollutants, you use a structural, microfounded model based on ecology, biology, and chemistry, not a simple linear model based on some quasi-experiment like a past pollution episode.

That doesn’t mean you don’t do the quasi-experimental studies, of course. You do them in order to check to make sure your structural models are good. If the structural climate model gets a volcanic eruption wrong, you know you have to go back and reexamine the model. If the structural ecological model gets a pollution episode wrong, you know you have to rethink the model’s assumptions. And so on.

(…)

Economics could, in principle, do the exact same thing. Suppose you want to predict the effects of labor policies like minimum wages, liberalization of migration, overtime rules, etc. You could make structural models, with things like search, general equilibrium, on-the-job learning, job ladders, consumption-leisure complementarities, wage bargaining, or whatever you like. Then you could check to make sure that the models agreed with the results of quasi-experimental studies – in other words, that they correctly predicted the results of minimum wage hikes, new overtime rules, or surges of immigration. Those structural models that failed to get the natural experiments wrong would be considered unfit for use, while those that got the natural experiments right would stay on the list of usable models. As time goes on, more and more natural experiments will shrink the set of usable models, while methodological innovations enlarges the set.

But in practice, I think what often happens in econ is more like the following:

1. Some papers make structural models, observe that these models can fit (or sort-of fit) a couple of stylized facts, and call it a day. Economists who like these theories (based on intuition, plausibility, or the fact that their dissertation adviser made the model) then use them for policy predictions forever after, without ever checking them rigorously against empirical evidence.

2. Other papers do purely empirical work, using simple linear models. Economists then use these linear models to make policy predictions (“Minimum wages don’t have significant disemployment effects”).

3. A third group of papers do empirical work, observe the results, and then make one structural model per paper to “explain” the empirical result they just found. These models are generally never used or seen again.

A lot of young, smart economists trying to make it in the academic world these days seem to write papers that fall into Group 3. This seems true in macro, at least, as Ricardo Reis shows in a recent essay. Reis worries that many of the theory sections that young smart economists are tacking on to the end of fundamentally empirical papers are actually pointless

(…)

It’s easy to see this pro-forma model-making as a sort of conformity signaling – young, empirically-minded economists going the extra mile to prove that they don’t think the work of the older “theory generation” (who are now their advisers, reviewers, editors and senior colleagues) was for naught.

But what is the result of all this pro-forma model-making? To some degree it’s just a waste of time and effort, generating models that will never actually be used for anything. It might also contribute to the “chameleon” problem, by giving policy advisers an effectively infinite set of models to pick and choose from.

And most worryingly, it might block smart young empirically-minded economists from using structural models the way other scientists do – i.e., from trying to make models with consistently good out-of-sample predictive power. If model-making becomes a pro-forma exercise you do at the end of your empirical paper, models eventually become a joke. Ironically, old folks’ insistence on constant use of theory could end up devaluing it.

(…)

In other words, econ seems too focused on “theory vs. evidence” instead of using the two in conjunction. And when they do get used in conjunction, it’s often in a tacked-on, pro-forma sort of way, without a real meaningful interplay between the two. Of course, this is just my own limited experience, and there are whole fields – industrial organization, environmental economics, trade – that I have relatively limited contact with. So I could be over-generalizing. Nevertheless, I see very few economists explicitly calling for the kind of “combined approach” to modeling that exists in other sciences – i.e., using evidence to continuously restrict the set of usable models.

The term “secular stagnation” has become a catch-all description for long-term economic pessimism. But it’s gotten confused with a very different idea — the technological stagnation hypothesis, proposed by economist Robert Gordon (and by Bloomberg View’s Tyler Cowen). These are two very different ideas. Both would lead to slow growth in the long term, but they imply different causes and different remedies.

Summers’ secular stagnation is all about aggregate demand. Normally, economists think of demand as something that falls temporarily in a recession and then bounces back. But the failure of many economies to return to their previous trends after big slowdowns has made some economists worry if demand shortfalls could be very persistent.

Demand gaps usually emerge when everyone tries to save money at the same time. This could happen because people become more pessimistic about the future, for example, or because they suddenly decide they need more liquid assets. But when everyone tries to hold onto cash, they don’t spend, and so companies don’t produce things. Companies that don’t produce things lay off workers, and pretty soon there’s a recession.

Usually this process ends naturally. Eventually people need to replace their old cars and fix up their houses, or their temporary bout of pessimism ends, or some other force acts to restore demand. But under certain conditions, in some models, it’s possible for an economy to trap itself, so that low demand and slow growth become a self-reinforcing, self-perpetuating cycle.

(…)

Technological stagnation is a different beast. According to Gordon and others, humanity has simply picked most of the low-hanging fruit of science and technology. Airplanes and cars travel no faster today than they did 50 years ago. Electricity, air conditioning and household appliances have made our homes about as pleasant as they’re likely to get, and so on. That doesn’t mean advances stop, but it means that each one is less game-changing than the last.

A key piece of the tech stagnation hypothesis is that production of the things we want isn’t going to get much cheaper. Gordon points to slowing productivity as evidence that our economy is getting worse at finding new ways to do more with less. This trend is worldwide, which makes sense, since a decline in science and technology should be global in nature.

So technological stagnation is all about supply, while secular stagnation is about demand. The two are related — slower productivity growth tends to reduce interest rates, putting the economy closer to the zero lower bound that drives demand shortages. But the two types of stagnation are very different things, requiring very different policy responses.

If we’re in secular stagnation, the economy is wasting its potential. Workers are staying home — not counted as officially unemployed, but out of the labor force completely — playing video games while offices sit empty and unused. In that case, we need something like fiscal stimulus to raise demand and lift us back to full employment.

But if we’re in technological stagnation, there’s not much we can do. Yes, there are some things government can do to boost innovation at the margin, like reforming patent laws, lifting onerous regulations, and investing in research and development. But in the long term, the forces of progress are difficult to predict and control. If we’ve already exploited the biggest innovations, we need to reconcile ourselves to living lives not much better than those of our parents. That would be a disappointing outcome, but it might be the best we can do.